docs: def smooth(x, window_len=10, window='hanning'):
      """
      Smooth the data using a window with requested size.
  
      This method is based on the convolution of a scaled window with the signal.
      The signal is prepared by introducing reflected copies of the signal
      (with the window size) in both ends so that transient parts are minimized
      in the begining and end part of the output signal.
  
      Parameters
      ----------
      x : numpy.array
          the input signal
      window_len : int
          the dimension of the smoothing window
      window : str
          the type of window from 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'
          flat window will produce a moving average smoothing.
  
      Returns
      -------
      The smoothed signal
  
      See Also
      -------
  
      numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman, numpy.convolve
      scipy.signal.lfilter
  
      Examples
      --------
  
      >>> import numpy as np
      >>> from timeside.plugins.grapher.utils import smooth
      >>> t = np.arange(-2,2,0.1)
      >>> x = np.sin(t)+np.random.randn(len(t))*0.1
      >>> y = smooth(x)
      >>> import matplotlib.pyplot as plt
      >>> plt.plot(x) # doctest: +SKIP
      [<matplotlib.lines.Line2D object at 0x...>]
      >>> plt.plot(y) # doctest: +SKIP
      [<matplotlib.lines.Line2D object at 0x...>]
      >>> plt.legend(['Source signal', 'Smoothed signal']) # doctest: +SKIP
      <matplotlib.legend.Legend object at 0x...>
      >>> plt.show() # doctest: +SKIP
      """
  
      # TODO: the window parameter could be the window itself if an array
      # instead of a string
  
      if x.ndim != 1:
          raise ValueError("smooth only accepts 1 dimension arrays.")
      if x.size < window_len:
          raise ValueError("Input vector needs to be bigger than window size.")
      if window_len < 3:
          return x
      if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:
          raise ValueError("Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'")
  
      s = numpy.r_[2 * x[0] - x[window_len:1:-1],
                   x, 2 * x[-1] - x[-1:-window_len:-1]]
  
      if window == 'flat':  # moving average
          w = numpy.ones(window_len, 'd')
      else:
          w = getattr(numpy, window)(window_len)
  
      y = numpy.convolve(w / w.sum(), s, mode='same')
      return y[window_len - 1:-window_len + 1]